17 citations · 55 across the 12 of their papers we have counts for
12 papers
Optimal 1-Wasserstein Distance for WGANs
Arthur Stéphanovitch, Ugo Tanielian, Benoît Cadre +2
The mathematical forces at work behind Generative Adversarial Networks raise challenging theoretical issues. Motivated by the important question of characterizing the geometrical p…
Some Theoretical Properties of GANs
G. Biau, B. Cadre, M. Sangnier +1
Generative Adversarial Networks (GANs) are a class of generative algorithms that have been shown to produce state-of-the art samples, especially in the domain of image creation. Th…
Accelerated Gradient Boosting
Gérard Biau, Benoît Cadre, Laurent Rouvìère
Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimiz…
Optimization by gradient boosting
Gérard Biau, Benoît Cadre
Gradient boosting is a state-of-the-art prediction technique that sequentially produces a model in the form of linear combinations of simple predictors---typically decision trees--…
A nonparametric test for Cox processes
Benoît Cadre, Gaspar Massiot, Lionel Truquet
In a functional setting, we propose two test statistics to highlight the Poisson nature of a Cox process when n copies of the process are available. Our approach involves a compari…
The Statistical Performance of Collaborative Inference
Gérard Biau, Kevin Bleakley, Benoit Cadre
The statistical analysis of massive and complex data sets will require the development of algorithms that depend on distributed computing and collaborative inference. Inspired by t…